Multi-sensor robotic mapping with thermal and acoustic imaging for non-contact technical diagnostics

Machine diagnostics is essential in maintaining high operational efficiency and minimizing machinery downtime. One way to achieve this goal is by implementing automated diagnostic systems. This paper presents a multi-sensor mapping system developed on a quadruped robotic platform. The robot combines diagnostic data from thermal and acoustic sensors with LiDAR data to create a 3D map of the working environment. Anomalous readings of temperature and sound pressure levels are identified and indicated on a 3D map with distinct color values through data fusion. Validation tests performed in a laboratory environment confirmed the feasibility of this approach by correctly identifying simulated cases of malfunctioning machinery. The system robustness was evaluated through repeatability tests. The proposed multi-modal mapping solution enables reliable monitoring of the technical state of machinery in complex environments.